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Integrated watershed modeling using interval valued fuzzy computations to enhance watershed restoration and protection at field-scale

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Abstract

Watershed models are scientific tools required not only for replicating the bio-physicochemical processes occurring on the landscape and water bodies, but also for addressing the complex decision-making process. This involves implementation of sustainable, economically feasible best management practices (BMPs) for watershed restoration and protection. Although numerous watershed models working at different spatio-temporal scales are available, it is often challenging for watershed modelers to identify the best ones which can simultaneously address agronomic, hydrologic, socio-economic and ecological objectives at field scale. Some of the popular models are Better Assessment Science Integrating Point and Nonpoint Sources, SWAT (Soil and Water Assessment Tool), Agriculture Conservation Planning Framework (ACPF), Prioritize Target and Measure Application (PTMApp), etc. In this study, we compare nine contemporary models with primary focus on assessing the ability of these models to achieve agronomic, hydrologic, socio-economic and ecological objectives of a watershed through Best Management Practices (BMPs) adopted at field scale. To achieve these fuzzy objectives, ten suitable criteria such as BMP feasibility, Total Maximum Daily Load scenario assessment, uncertainty analysis, climate change incorporation, cost-effectiveness, field-scale implementation, hydrology replication, stakeholder involvement, etc. have been considered. Modified form of traditional fuzzy computations namely interval-valued fuzzy computations has been used to compare various models based on these criteria. Integrated decision support system (DSS) has been developed that takes into account the subjective and uncertain perceptions of watershed modelers and decision-making bodies using fuzzy logic. Study presents two novel aspects viz. (i) Usage of interval-valued fuzzy sets to provide additional freedom or flexibility to stakeholders when making decisions related to watershed management, and (ii) proposing unique integration of three models namely PTMApp, HSPF-SAM and ACPF to achieve watershed planning and management objectives in a cost-effective manner. Although study includes watershed experts from Indian watersheds, the DSS is flexible and replicable to any other watershed across the globe.

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References

  • Afshar A, Mariño MA, Saadatpour M, Afshar A (2011) Fuzzy TOPSIS multi-criteria decision analysis applied to Karun reservoirs system. Water Resour Manag 25(2):545–563

    Article  Google Scholar 

  • Ahmadisharaf E, Camacho RA, Zhang HX, Hantush MM, Mohamoud YM (2019) Calibration and validation of watershed models and advances in uncertainty analysis in TMDL studies. J Hydrol Eng 24(7):03119001

    Article  Google Scholar 

  • Alilou H, Rahmati O, Singh VP, Choubin B, Pradhan B, Keesstra S, Ghiasi SS, Sadeghi SH (2019) Evaluation of watershed health using fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria. J Environ Manag 232:22–36

    Article  Google Scholar 

  • Amin MM, Veith TL, Shortle JS, Karsten HD, Kleinman PJ (2020) Addressing the spatial disconnect between national-scale total maximum daily loads and localized land management decisions. J Environ Qual 49(3):613–627

    Article  CAS  Google Scholar 

  • Arabi M, Govindaraju RS, Hantush MM (2006) Cost‐effective allocation of watershed management practices using a genetic algorithm. Water Resour Res 42(10)

  • Borah DK, Ahmadisharaf E, Padmanabhan G, Imen S, Mohamoud YM (2019) Watershed models for development and implementation of total maximum daily loads. J Hydrol Eng 24(1):03118001

    Article  Google Scholar 

  • Bradford L, Thapa A, Duffy A, Hassanzadeh E, Strickert G, Noble B, Lindenschmidt KE (2019) Incorporating social dimensions in hydrological and water quality modeling to evaluate the effectiveness of agricultural beneficial management practices in a Prairie River Basin. Environ Sci Pollut Res 1–17

  • BWSR-IWI (2019) Prioritize, target, measure application (PTMApp) desktop toolbar user’s guide. https://ptmapp.bwsr.state.mn.us/files/PTMApp_User_Guide.pdf

  • Cázares-Rodríguez JE, Vivoni ER, Mascaro G (2017) Comparison of two watershed models for addressing stakeholder flood mitigation strategies: Case study of Hurricane Alex in Monterrey. Mex J Hydrol Eng 22(9):05017018

    Article  Google Scholar 

  • Chen SM, Lee LW (2010) A fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Syst Appl 37:2790–2798

    Article  Google Scholar 

  • Cho SJ, Wilcock PR, Belmont P, Gran KB, Hobbs BF (2019) Simulation model for collaborative decision making on sediment source reduction in an intensively managed watershed. Water Resour Res 55(2):1544–1564

    Article  Google Scholar 

  • Cochrane TA, Yoder DC, Flanagan DC, Dabney SM (2019) Quantifying and modeling sediment yields from interrill erosion under armouring. Soil Tillage Res 195:104375

    Article  Google Scholar 

  • Dagnew A, Scavia D, Wang YC, Muenich R, Long C, Kalcic M (2019) Modeling flow, nutrient, and sediment delivery from a large international watershed using a field-scale SWAT model. JAWRA J Am Water Resour Assoc 55(5):1288–2130

    Article  Google Scholar 

  • Dudula J, Randhir TO (2016) Modeling the influence of climate change on watershed systems: adaptation through targeted practices. J Hydrol 541:703–713

    Article  Google Scholar 

  • Elshorbagy A (2006) Multicriterion decision analysis approach to assess the utility of watershed modeling for management decisions. Water Resour Res 42(9)

  • Epelde AM, Antiguedad I, Brito D, Jauch E, Neves R, Garneau C, Sánchez-Pérez JM (2016) Different modelling approaches to evaluate nitrogen transport and turnover at the watershed scale. J Hydrol 539:478–494

    Article  CAS  Google Scholar 

  • Esteve P, Varela-Ortega C, Blanco-Gutiérrez I, Downing TE (2015) A hydro-economic model for the assessment of climate change impacts and adaptation in irrigated agriculture. Ecol Econ 120:49–58

    Article  Google Scholar 

  • Fonseca A, Ames DP, Yang P, Botelho C, Boaventura R, Vilar V (2014) Watershed model parameter estimation and uncertainty in data-limited environments. Environ Model Softw 51:84–93

    Article  Google Scholar 

  • Frost W, Lott RC, LaPlante R, Rose F (2019) Modeling for TMDL implementation. J Hydrol Eng 24(6):05019010

    Article  Google Scholar 

  • Gao C, Liu L, Ma D, He K, Xu YP (2019) Assessing responses of hydrological processes to climate change over the southeastern Tibetan Plateau based on resampling of future climate scenarios. Sci Total Environ 664:737–752

    Article  CAS  Google Scholar 

  • Haider H, Singh P, Ali W, Tesfamariam S, Sadiq R (2015) Sustainability evaluation of surface water quality management options in developing countries: multicriteria analysis using fuzzy UTASTAR method. Water Resour Manag 29(8):2987–3013

    Article  Google Scholar 

  • HEI (2018) A report on ‘targeted implementation plan for the pomme de terre river watershed to improve surface water quality’

  • ICAR (2019) State specific strategies for doubling of farmers income- 2022. Indian Council of Agricultural Research (ICAR), New Delhi

  • Jha MK, Wolter CF, Schilling KE, Gassman PW (2010) Assessment of total maximum daily load implementation strategies for nitrate impairment of the Raccoon River, Iowa. J Environ Qual 39(4):1317–1327

    Article  CAS  Google Scholar 

  • Kang MS, Park SW, Lee JJ, Yoo KH (2006) Applying SWAT for TMDL programs to a small watershed containing rice paddy fields. Agric Water Manag 79(1):72–92

    Article  Google Scholar 

  • Lewandowski AM, Tomer MD, Buchanan J, Kiel A, Sloan JJ, Olson L, Power R (2020) Agricultural conservation planning framework: watershed applications, research opportunities, and training resources. J Soil Water Conserv 75(4):427–433

    Article  Google Scholar 

  • Li X, Cheng G, Lin H, Cai X, Fang M, Ge Y et al (2018) Watershed system model: the essentials to model complex human-nature system at the river basin scale. J Geophys Res Atmos 123:3019–3034. https://doi.org/10.1002/2017JD028154

    Article  Google Scholar 

  • Lindblom J, Lundström C, Ljung M, Jonsson A (2017) Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies. Precis Agric 18(3):309–331

    Article  Google Scholar 

  • Liu P, Zhang S, Shang M (2021) Effect of the membership function type on the fuzzy risk of allowable groundwater drawdown calculation results. Stoch Environ Res Risk Assess 1–12

  • Luo C, Li Z, Li H, Chen X (2015) Evaluation of the AnnAGNPS model for predicting runoff and nutrient export in a typical small watershed in the hilly region of Taihu Lake. Int J Environ Res Public Health 12(9):10955–10973

    Article  CAS  Google Scholar 

  • Marsili-Libelli S, Giusti E (2008) Water quality modelling for small river basins. Environ Model Softw 23(4):451–463

    Article  Google Scholar 

  • McLellan EL, Schilling KE, Wolter CF et al (2018) Right practice, right place: a conservation planning toolbox for meeting water quality goals in the corn belt. J Soil Water Conserv 73(2):29A-34A

    Article  Google Scholar 

  • Merriman KR, Daggupati P, Srinivasan R, Hayhurst B (2019) Assessment of site-specific agricultural best management practices in the upper east river watershed, wisconsin, using a field-scale SWAT model. J Great Lakes Res 45(3):619–641

    Article  CAS  Google Scholar 

  • Minatour Y, Bonakdari H, Zarghami M, Ali BM (2015) Water supply management using an extended group fuzzy decision-making method: a case study in North-Eastern Iran. Appl Water Sci 5(3):291–304

    Article  Google Scholar 

  • Mori K, Tada K, Tawara Y et al (2015) Integrated watershed modeling for simulation of spatiotemporal redistribution of post-fallout radionuclides: application in radiocesium fate and transport processes derived from the Fukushima accidents. Environ Model Softw 72:126–146

    Article  Google Scholar 

  • MPCA (2017) HSPF scenario application manager (SAM) user’s reference manual. https://respec.sharefile.com/share/view/51007f876a3a4ebd

  • Muhammad A, Evenson GR, Stadnyk TA et al (2019) Impact of model structure on the accuracy of hydrological modeling of a Canadian Prairie watershed. J Hydrol Reg Stud 21:40–56

    Article  Google Scholar 

  • Nasta P, Penna D, Brocca L, Zuecco G, Romano N (2018) Downscaling near-surface soil moisture from field to plot scale: a comparative analysis under different environmental conditions. J Hydrol 557:97–108

    Article  Google Scholar 

  • NMCG (2014) Reform imperatives for agricultural sustainability in Ganga Basin. National Mission for Clean Ganga (NMCG) Ministry of Water Resources, River Development & Ganga Rejuvenation Govt. of India, New Delhi

  • Oroji B, Karimi ZF, Bahrami H (2017) Determine of surface water quality index in Iran. J Environ Earth Sci 7(2):53–63

    Google Scholar 

  • Pai N, Saraswat D, Srinivasan R (2012) Field_SWAT: a tool for mapping SWAT output to field boundaries. Comput Geosci 40:175–184

    Article  Google Scholar 

  • Pan Q, Chhipi-Shrestha G et al (2017) Evaluating water reuse applications under uncertainty: generalized intuitionistic fuzzy-based approach. Stoch Env Res Risk Assess 32(4):1099–1111

    Article  Google Scholar 

  • Pan Q, Chhipi-Shrestha G, Zhou D, Zhang K, Hewage K, Sadiq R (2018) Evaluating water reuse applications under uncertainty: generalized intuitionistic fuzzy-based approach. Stoch Environ Res Risk Assess 32(4):1099–1111

    Article  Google Scholar 

  • Parajuli PB, Nelson NO, Frees LD, Mankin KR (2009) Comparison of AnnAGNPS and SWAT model simulation results in USDA-CEAP agricultural watersheds in south- central Kansas. Hydrol Process Int J 23(5):748–763

    Article  CAS  Google Scholar 

  • Park RA, Clough JS, Wellman MC (2008) AQUATOX: modeling environmental fate and ecological effects in aquatic ecosystems. Ecol Model 213(1):1–15

    Article  CAS  Google Scholar 

  • Porter SA, Tomer MD, James DE, Boomer KMB (2018) Agricultural conservation planning framework ArcGIS toolbox user’s manual. National Laboratory for Agriculture & the Environment, USDA-ARS Ames, Iowa

  • Ranjan P, Singh AS, Tomer MD, Lewandowski AM, Prokopy LS (2019) Lessons learned from using a decision-support tool for precision placement of conservation practices in six agricultural watersheds in the US midwest. J Environ Manag 239:57–65

    Article  Google Scholar 

  • RazaviToosi SL, Samani JMV (2019) A Fuzzy group decision making framework based on ISM-FANP-FTOPSIS for evaluating watershed management strategies. Water Resour Manage 33(15):5169–5190

    Article  Google Scholar 

  • Rousta BA, Araghinejad S (2015) Development of a multi criteria decision making tool for a water resources decision support system. Water Resour Manag 29(15):5713–5727

    Article  Google Scholar 

  • Rundhaug TJ, Geimer GR, Drake CW, Amado AA, Bradley AA, Wolter CF, Weber LJ (2018) Agricultural conservation practices in Iowa watersheds: comparing actual implementation with practice potential. Environ Monit Assess 190(11):1–7

    Article  Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    Google Scholar 

  • Santy S, Mujumdar P, Bala G (2020) Potential impacts of climate and land use change on the water quality of Ganga River around the industrialized Kanpur region. Sci Rep 10(1):1–13

    Article  CAS  Google Scholar 

  • Shen ZY, Gong YW, Li YH et al (2009) A comparison of WEPP and SWAT for modeling soil erosion of the zhangjiachong watershed in the three gorges reservoir area. Agric Water Manag 96(10):1435–1442

    Article  Google Scholar 

  • Singh VP (2018) Hydrologic modeling: progress and future directions. Geosci Lett 5(1):1–18

    Article  Google Scholar 

  • Singh AP, Ghosh SK, Sharma P (2007) Water quality management of a stretch of river Yamuna: an interactive fuzzy multi-objective approach. Water Resour Manag 21(2):515–532

    Article  Google Scholar 

  • Srinivas R, Singh AP (2018a) An integrated fuzzy-based advanced eutrophication simulation model to develop the best management scenarios for a river basin. Environ Sci Pollut Res 25(9):9012–9039

    Article  Google Scholar 

  • Srinivas R, Singh AP (2018b) Impact assessment of industrial wastewater discharge in a river basin using interval-valued fuzzy group decision-making and spatial approach. Environ Dev Sustain 20(5):2373–2397

    Article  Google Scholar 

  • Srinivas R, Bhakar P, Singh AP (2015) Groundwater quality assessment in some selected area of Rajasthan, India using fuzzy multi-criteria decision making tool. Aquat Procedia 4:1023–1030

  • Srinivas R, Singh AP, Sharma R (2017) A scenario-based impact assessment of trace metals on ecosystem of river Ganges using multivariate analysis coupled with fuzzy decision-making approach. Water Resour Manag 31(13):4165–4185

    Article  Google Scholar 

  • Srinivas R, Singh AP, Dhadse K, Garg C, Deshmukh A (2018) Sustainable management of a river basin by integrating an improved fuzzy based hybridized SWOT model and geo-statistical weighted thematic overlay analysis. J Hydrol 563:92–105

    Article  Google Scholar 

  • Srinivas R, Drewitz M, Magner J (2020a) Evaluating watershed-based optimized decision support framework for conservation practice placement in Plum Creek Minnesota. J Hydrol 583:124573

    Article  Google Scholar 

  • Srinivas R, Singh AP, Dhadse K, Garg C (2020b) An evidence based integrated watershed modelling system to assess the impact of non-point source pollution in the riverine ecosystem. J Clean Product 246:118963

    Article  CAS  Google Scholar 

  • Sudheer KP, Lakshmi G, Chaubey I (2011) Application of a pseudo simulator to evaluate the sensitivity of parameters in complex watershed models. Environ Model Softw 26(2):135–143

    Article  Google Scholar 

  • Sunde MG, He HS, Hubbart JA, Urban MA (2018) An integrated modeling approach for estimating hydrologic responses to future urbanization and climate changes in a mixed-use midwestern watershed. J Environ Manag 220:149–162

    Article  Google Scholar 

  • Tang T, Strokal M, van Vliet MT et al (2019) Bridging global, basin and local-scale water quality modeling towards enhancing water quality management worldwide. Curr Opin Environ Sustain 36:39–48

    Article  Google Scholar 

  • Tegegne G, Kim YO, Seo SB, Kim Y (2019) Hydrological modelling uncertainty analysis for different flow quantiles: a case study in two hydro-geographically different watersheds. Hydrol Sci J 64(4):473–489

    Article  Google Scholar 

  • Tomer MD, Boomer KMB, Porter SA, Gelder BK, James DE, McLellan E (2015) Agricultural conservation planning framework: 2. Classification of riparian buffer design types with application to assess and map stream corridors. J Environ Qual 44(3):768–779

    Article  CAS  Google Scholar 

  • Von Gunten D, Wöhling T, Haslauer CP, Merchán D, Causapé J, Cirpka OA (2015) Estimating climate-change effects on a Mediterranean catchment under various irrigation conditions. J Hydrol Reg Stud 4:550–570

    Article  Google Scholar 

  • Wang X, Kemanian AR, Williams JR (2011) Special features of the EPIC and APEX modeling package and procedures for parameterization, calibration, validation, and applications. Methods Introd Syst Models Agric Res 2:177–208

    CAS  Google Scholar 

  • Wang X, Yen H, Jeong J, Williams JR (2016) Accounting for conceptual soil erosion and sediment yield modeling uncertainty in the APEX model using Bayesian model averaging. J Hydrol Eng 20(6):C4014010

    Article  Google Scholar 

  • Whittemore RC, Beebe J (2000) EPA’S basins model: good science or serendipitous modeling? 1. JAWRA J Am Water Resour Assoc 36(3):493–499

    Article  CAS  Google Scholar 

  • Xu TY, Qin XS (2015) A sequential fuzzy model with general-shaped parameters for water supply–demand analysis. Water Resour Manag 29(5):1431–1446

    Article  Google Scholar 

  • Zoltay VI, Vogel RM, Kirshen PH, Westphal KS (2010) Integrated watershed management modeling: generic optimization model applied to the Ipswich River Basin. J Water Resour Plan Manag 136(5):566–575

    Article  Google Scholar 

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  1. Birla Institute of Technology and Science, Pilani, Rajasthan, India

    Rallapalli Srinivas, Brajeswar Das & Anupam Singhal

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  1. Rallapalli Srinivas
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  2. Brajeswar Das
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  3. Anupam Singhal
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Contributions

Rallapalli Srinivas: Conceptualization, Methodology, Software, Writing- Original draft preparation, Investigation, Writing- Reviewing and Editing Visualization, Supervision. Brajeswar Das: Data curation, Writing- Original draft preparation, Investigation, Methodology. Anupam Singhal: Writing- Reviewing and Editing, Conceptualization, Visualization.

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Correspondence to Rallapalli Srinivas.

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Srinivas, R., Das, B. & Singhal, A. Integrated watershed modeling using interval valued fuzzy computations to enhance watershed restoration and protection at field-scale. Stoch Environ Res Risk Assess 36, 1429–1445 (2022). https://doi.org/10.1007/s00477-021-02151-5

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